Dataflight provides an AI‑driven platform that automates volumetric segmentation, feature extraction, and quantitative measurement of 3D bioimaging data such as microscopy, MRI, and CT scans. Users upload raw image volumes and receive annotated 3D visualizations and exportable structured data via a web interface or API, with adaptive model selection and cloud‑scale processing to handle terabyte‑scale datasets.
Funding
Funding not disclosed
Founders
Product
Problem
Researchers and clinicians working with 3D bioimaging data (e.g., microscopy, MRI, CT) face bottlenecks in processing, segmenting, and interpreting large volumetric datasets, which often require extensive manual effort and specialized expertise.
Solution
Dataflight offers an AI-driven platform that automates the analysis of 3D bioimaging data. The system leverages agentic AI models to perform tasks such as volumetric segmentation, feature extraction, and quantitative measurement without extensive user intervention. Users upload raw imaging volumes, and the platform orchestrates a pipeline of pretrained models that adapt to the specific tissue type and imaging modality. Results are delivered as annotated 3D visualizations and structured data that can be exported to downstream analysis tools or integrated into laboratory information systems. The service is accessible via a web interface and API, enabling both interactive exploration and programmatic integration into existing research workflows.
Target Audience
Primary customers are biomedical researchers, pharmaceutical R&D teams, and clinical imaging departments that need high‑throughput, accurate analysis of 3D microscopy, MRI, or CT datasets.
Features
- Automated volumetric segmentation using deep‑learning agents tuned for microscopy, MRI, and CT data
- Adaptive model selection that customizes analysis pipelines based on image modality and tissue context
- Interactive 3D viewer with overlay of AI‑generated annotations and quantitative metrics
- API and SDK for batch processing and integration with LIMS, Jupyter notebooks, or custom pipelines
- Cloud‑native infrastructure that scales compute resources to handle terabyte‑scale datasets
- Export of results in standard formats (e.g., NIfTI, OME‑Tiff, CSV) for downstream statistical analysis